The human face plays a central role in most forms of natural human interaction so we may expect that computational methods for analysis of facial information, modeling of internal emotional states, and methods for graphical synthesis of faces and facial expressions will play a growing role in human-computer and human-robot interaction. However, certain areas of face-based HCI, such as facial expression recognition and robotic facial display have lagged others, such as eye-gaze tracking, facial recognition, and conversational characters. Our goal in this paper is to review the situation in HCI with regards to the human face, and to discuss strategies, which could bring more slowly developing areas up to speed. In particular, we are proposing the “The Art of the Soluble” as a strategy forward and provide examples that successfully applied this strategy.
Analysis: Facial Expression Classification
The attractive prospect of being able to gain insight into a user’s affective state may be considered one of the key unsolved problems in HCI. It is known that it is difficult to measure the “valence” component of affective state, as compared to “arousal”, which may be gauged using biosensors. However, a smile, or frown, provides a clue that goes beyond physiological measurements. It is also attractive that expressions can be guaged non-invasively with inexpensive video cameras.
Key Terms in this Chapter
Weak A.I. (contrast with hard A.I.): This term has connotations in the context of practical work in artificial intelligence, as well as for theoretical studies of A.I. and the philosophy of mind. In the current article we are primarily concerned with the former usage of the term, namely with that domain of approaches to machine intelligence which do not take, as a primary goal, an attempt to match or exceed human intelligence, this latter goal being the hallmark of “strong A.I.” research.
Expressive Robots: are robots that use facial expressions, gestures, posture and speech to communicate with the human user. This communication might not only include factual information, but also emotional states.
Artificial Expressions: This term relates to a somewhat radical proposal to reframe the goals of affective computing towards the construction of new machine-mediated channels for the communication of affect between humans, or artificial expressions as we call them. The affected intended by these artificial expressions is not to be defined a prior, but to be learned and evolved through ongoing situational interaction in human-machine-human communication.
Human-Robot Interaction (HRI): Is the study of interactions between people (users) and robots. HRI is multidisciplinary with contributions from the fields of human-computer interaction, artificial intelligence, robotics, natural language understanding, and several social sciences.
Facial Expression Classification: In machine vision, the automatic labelling of facial images or sequences of images with a semantic label or labels describing affect portrayed by the face. Our paper suggests that research has come to focus on a narrowly defined version of this problem: namely the hard classification of facial images (or sequences) into the stereotypical Ekman universal facial expressions, and that researchers in pattern recognition and human-computer interaction could profit by more broadly framing the research domain.
Art of the Soluble (AOTS): Scientific research strategy advocated by Nobel laureate biologist Peter Medawar. Specifically, the AOTS strategy emphasizes skill in in the recognition of scientific problems which have not yet been solved but are reasonably amenable to solution with reasonable time and resources. Here we have suggested that, in some cases, the introduction of facial expression technology into HCI may be hindered by excessive concentration on research problems which fall into the domain of strong A.I. and that it is time to consider AOTS approaches.